Prof. Petia Radeva
University of Barcelona, Spain
Addressing the food image challenge by uncertainty modeling and single-to-multi-label food recognition
Transfer learning can be attributed to several recent breakthroughs in deep learning. It has shown upbeat performance improvements, but most of the transfer learning applications are confined towards fine-tuning. Transfer learning facilitates the learnability of the networks on domains with less data. However, learning becomes a difficult task with complex domains, such as multi-label food recognition, owing to the number of food classes as well as to the fine-grained nature of food images. In such situations, we also can observe Negative Transfer. In this talk we will introduce a transfer learning framework to avoid negative transfer and to leverage the knowledge learnt on a simpler single-label food recognition task onto multi-label food recognition. We will show that negative transfer is also related to uncertainty of the classes. After introducing different methods for uncertainty modelling in Deep learning we will show how it can be useful to guide the process of data augmentation and classifier improvement.
Prof. Petia Raveda is a Full professor at the Universitat de Barcelona (UB), Head of the Consolidated Research Group “Computer Vision and Machine Learning” at the University of Barcelona (CVMLUB) and Senior researcher in UB's Computer Vision Center. She was PI of UB in 7 European, 3 international and more than 25 national projects devoted to applying Computer Vision and Machine learning for real problems like food intake monitoring (e.g. for patients with kidney transplants and for older people). Petia Radeva is a REA-FET-OPEN vice-chair since 2015 on, and international mentor in the Wild Cards EIT program since 2017.
She is an Associate editor of Pattern Recognition journal (Q1, IP=7.196) and International Journal of Visual Communication and Image Representation (Q2, IP=3.13). She supervised 22 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings, her Google scholar h-index is 49 with more than 9000 cites. She is a Research Manager of the State Agency of Research (Agencia Estatal de Investigación, AEI) of the Ministry of Science and Innovation of Spain.
Petia Radeva became an IAPR Fellow in 2015, ICREA Academia assigned to the 30 best scientists in Catalonia for her scientific merits since 2014, received several international awards (“Aurora Pons Porrata” of CIARP, Prize “Antonio Caparrós” for the best technology transfer of UB, etc).